Stabak Roy Thank you very much for your detailed reply. It has helped me a lot. I think I'll have to transform my data, but I'll follow your script.

Thank you and kind regards

The SADIE methodology is a suitable approach to study the spatial distribution patterns of individuals within a plot. It is a distance-based method that examines the spatial structure of a point pattern relative to a hypothetical random distribution.

There are several R packages that implement the SADIE methodology, such as "spatstat" and "spatcounts". These packages provide a range of tools for analyzing spatial point patterns, including calculating various distance indices and performing hypothesis tests for spatial randomness.

To apply the SADIE methodology to your study, you can use the "spatstat" package in R to compute distance indices such as the nearest neighbor distance, Ripley's K-function, and the pair correlation function for each year of sampling. You can then compare the distance indices across years to detect any changes in the spatial distribution pattern of the plant species.

In addition to SADIE methodology, you may also want to consider other spatial analysis techniques such as spatial autocorrelation and cluster analysis to further investigate the spatial patterns and changes over time.

Overall, the "spatstat" and "spatcounts" packages in R provide a comprehensive set of tools for analyzing spatial point patterns and should be useful for your study.

Hazrin Hasim Thank you for your reply. I will explore the packages you cite.